The Community for Technology Leaders
Green Image
Issue No. 10 - Oct. (2018 vol. 30)
ISSN: 1041-4347
pp: 1887-1900
Qi Song , School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA
Yinghui Wu , School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA
Peng Lin , School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA
Luna Xin Dong , Amazon. Inc., Seattle, WA
Hui Sun , Renmin University, Beijing, China
ABSTRACT
Querying heterogeneous and large-scale knowledge graphs is expensive. This paper studies a graph summarization framework to facilitate knowledge graph search. (1) We introduce a class of reduced summaries. Characterized by approximate graph pattern matching, these summaries are capable of summarizing entities in terms of their neighborhood similarity up to a certain hop, using small and informative graph patterns. (2) We study a diversified graph summarization problem. Given a knowledge graph, it is to discover top- $_$k$_$ summaries that maximize a bi-criteria function, characterized by both informativeness and diversity. We show that diversified summarization is feasible for large graphs, by developing both sequential and parallel summarization algorithms. (a) We show that there exists a 2-approximation algorithm to discover diversified summaries. We further develop an anytime sequential algorithm which discovers summaries under resource constraints. (b) We present a new parallel algorithm with quality guarantees. The algorithm is parallel scalable, which ensures its feasibility in distributed graphs. (3) We also develop a summary-based query evaluation scheme, which only refers to a small number of summaries. Using real-world knowledge graphs, we experimentally verify the effectiveness and efficiency of our summarization algorithms, and query processing using summaries.
INDEX TERMS
Parallel algorithms, Motion pictures, Scalability, Data mining, Query processing, Knowledge based systems, Time factors
CITATION

Q. Song, Y. Wu, P. Lin, L. X. Dong and H. Sun, "Mining Summaries for Knowledge Graph Search," in IEEE Transactions on Knowledge & Data Engineering, vol. 30, no. 10, pp. 1887-1900, 2018.
doi:10.1109/TKDE.2018.2807442
181 ms
(Ver 3.3 (11022016))